refactor: 修改chat 主流程逻辑
This commit is contained in:
parent
7bd19a7529
commit
6aea98554f
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@ -33,3 +33,9 @@ if MODELS and not DEFAULT_MODEL:
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# Max agentic loop iterations (tool call rounds)
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MAX_ITERATIONS = _cfg.get("max_iterations", 5)
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# Max parallel workers for tool execution (ThreadPoolExecutor)
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TOOL_MAX_WORKERS = _cfg.get("tool_max_workers", 4)
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# Max character length for a single tool result content (truncated if exceeded)
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TOOL_RESULT_MAX_LENGTH = _cfg.get("tool_result_max_length", 4096)
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@ -1,8 +1,11 @@
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"""Chat completion service"""
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import json
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import logging
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import uuid
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from flask import current_app, g, Response, request as flask_request
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from typing import Optional, Union
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from flask import current_app, Response, request as flask_request
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from werkzeug.exceptions import ClientDisconnected
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import requests
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from backend import db
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from backend.models import Conversation, Message
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from backend.tools import registry, ToolExecutor
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@ -11,14 +14,15 @@ from backend.utils.helpers import (
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build_messages,
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)
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from backend.services.llm_client import LLMClient
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from backend.config import MAX_ITERATIONS
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from backend.config import MAX_ITERATIONS, TOOL_MAX_WORKERS, TOOL_RESULT_MAX_LENGTH
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logger = logging.getLogger(__name__)
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def _client_disconnected():
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"""Check if the client has disconnected."""
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try:
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stream = flask_request.input_stream
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# If input_stream is unavailable, assume still connected
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if stream is None:
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return False
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return stream.closed
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@ -26,151 +30,111 @@ def _client_disconnected():
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return False
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def _sse_event(event: str, data: dict) -> str:
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"""Format a Server-Sent Event string."""
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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class ChatService:
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"""Chat completion service with tool support"""
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def __init__(self, llm: LLMClient):
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self.llm = llm
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def stream_response(
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self,
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conv: Conversation,
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tools_enabled: bool = True,
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project_id: str = None,
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tool_choice: Optional[Union[str, dict]] = None,
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):
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"""Stream response with tool call support.
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def stream_response(self, conv: Conversation, tools_enabled: bool = True, project_id: str = None):
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"""Stream response with tool call support
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Uses 'process_step' events to send thinking and tool calls in order,
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allowing them to be interleaved properly in the frontend.
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Args:
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conv: Conversation object
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tools_enabled: Whether to enable tools
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project_id: Project ID for workspace isolation
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tool_choice: Optional tool_choice override (e.g. "auto", "required", or dict)
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"""
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conv_id = conv.id
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conv_model = conv.model
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app = current_app._get_current_object()
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tools = registry.list_all() if tools_enabled else None
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initial_messages = build_messages(conv, project_id)
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# Create per-request executor for thread-safe isolation.
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# Each request gets its own _call_history and _cache, eliminating
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# race conditions when multiple conversations stream concurrently.
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executor = ToolExecutor(registry=registry)
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# Build context for tool execution
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context = {"model": conv_model}
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if project_id:
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context["project_id"] = project_id
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elif conv.project_id:
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context["project_id"] = conv.project_id
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def generate():
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messages = list(initial_messages)
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all_tool_calls = []
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all_tool_results = []
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all_steps = [] # Collect all ordered steps for DB storage (thinking/text/tool_call/tool_result)
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step_index = 0 # Track global step index for ordering
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total_completion_tokens = 0 # Accumulated across all iterations
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prompt_tokens = 0 # Not accumulated — last iteration's value is sufficient
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# (each iteration re-sends the full context, so earlier
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# prompts are strict subsets of the final one)
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all_steps = []
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step_index = 0
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total_completion_tokens = 0
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total_prompt_tokens = 0
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for iteration in range(MAX_ITERATIONS):
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full_content = ""
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full_thinking = ""
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token_count = 0
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msg_id = str(uuid.uuid4())
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tool_calls_list = []
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# Streaming step tracking — step ID is assigned on first chunk arrival.
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# thinking always precedes text in GLM's streaming order, so text gets step_index+1.
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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try:
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with app.app_context():
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active_conv = db.session.get(Conversation, conv_id)
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resp = self.llm.call(
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model=active_conv.model,
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messages=messages,
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max_tokens=active_conv.max_tokens,
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temperature=active_conv.temperature,
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thinking_enabled=active_conv.thinking_enabled,
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tools=tools,
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stream=True,
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)
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resp.raise_for_status()
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# Stream LLM response chunk by chunk
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for line in resp.iter_lines():
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# Early exit if client has disconnected
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if _client_disconnected():
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resp.close()
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return
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if not line:
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continue
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line = line.decode("utf-8")
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if not line.startswith("data: "):
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continue
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data_str = line[6:]
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if data_str == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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except json.JSONDecodeError:
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continue
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# Extract usage first (present in last chunk when stream_options is set)
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usage = chunk.get("usage", {})
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if usage:
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token_count = usage.get("completion_tokens", 0)
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prompt_tokens = usage.get("prompt_tokens", 0)
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choices = chunk.get("choices", [])
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if not choices:
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continue
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delta = choices[0].get("delta", {})
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# Accumulate thinking content for this iteration
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reasoning = delta.get("reasoning_content", "")
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if reasoning:
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full_thinking += reasoning
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if thinking_step_id is None:
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thinking_step_id = f'step-{step_index}'
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thinking_step_idx = step_index
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yield f"event: process_step\ndata: {json.dumps({'id': thinking_step_id, 'index': thinking_step_idx, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
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# Accumulate text content for this iteration
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text = delta.get("content", "")
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if text:
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full_content += text
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if text_step_id is None:
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text_step_idx = step_index + (1 if thinking_step_id is not None else 0)
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text_step_id = f'step-{text_step_idx}'
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yield f"event: process_step\ndata: {json.dumps({'id': text_step_id, 'index': text_step_idx, 'type': 'text', 'content': full_content}, ensure_ascii=False)}\n\n"
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# Accumulate tool calls from streaming deltas
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tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list)
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stream_result = self._stream_llm_response(
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app, conv_id, messages, tools, tool_choice, step_index
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)
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except requests.exceptions.HTTPError as e:
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resp = e.response
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if resp is not None and resp.status_code >= 500:
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yield _sse_event("error", {"content": f"LLM service unavailable ({resp.status_code})"})
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elif resp is not None and resp.status_code == 429:
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yield _sse_event("error", {"content": "Rate limit exceeded, please try again later"})
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else:
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yield _sse_event("error", {"content": f"LLM request failed: {e}"})
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return
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except requests.exceptions.ConnectionError:
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yield _sse_event("error", {"content": "Unable to connect to LLM service"})
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return
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except requests.exceptions.Timeout:
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yield _sse_event("error", {"content": "LLM request timed out"})
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return
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except Exception as e:
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yield f"event: error\ndata: {json.dumps({'content': str(e)}, ensure_ascii=False)}\n\n"
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logger.exception("Unexpected error during LLM streaming")
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yield _sse_event("error", {"content": f"Internal error: {e}"})
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return
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# --- Finalize: save thinking/text steps to all_steps for DB storage ---
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# No need to yield to frontend — incremental process_step events already sent.
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if stream_result is None:
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return # Client disconnected
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full_content, full_thinking, tool_calls_list, \
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thinking_step_id, thinking_step_idx, \
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text_step_id, text_step_idx, \
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completion_tokens, prompt_tokens, \
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sse_chunks = stream_result
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total_prompt_tokens += prompt_tokens
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total_completion_tokens += completion_tokens
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# Yield accumulated SSE chunks to frontend
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for chunk in sse_chunks:
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yield chunk
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# Save thinking/text steps to all_steps for DB storage
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if thinking_step_id is not None:
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all_steps.append({
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'id': thinking_step_id, 'index': thinking_step_idx,
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'type': 'thinking', 'content': full_thinking,
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"id": thinking_step_id, "index": thinking_step_idx,
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"type": "thinking", "content": full_thinking,
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})
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step_index += 1
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if text_step_id is not None:
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all_steps.append({
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'id': text_step_id, 'index': text_step_idx,
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'type': 'text', 'content': full_content,
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"id": text_step_id, "index": text_step_idx,
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"type": "text", "content": full_content,
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})
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step_index += 1
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@ -178,127 +142,79 @@ class ChatService:
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if tool_calls_list:
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all_tool_calls.extend(tool_calls_list)
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# Phase 1: emit all tool_call steps (before execution)
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# Emit tool_call steps (before execution)
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for tc in tool_calls_list:
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call_step = {
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'id': f'step-{step_index}',
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'index': step_index,
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'type': 'tool_call',
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'id_ref': tc['id'],
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'name': tc['function']['name'],
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'arguments': tc['function']['arguments'],
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"id": f"step-{step_index}",
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"index": step_index,
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"type": "tool_call",
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"id_ref": tc["id"],
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"],
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}
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all_steps.append(call_step)
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yield f"event: process_step\ndata: {json.dumps(call_step, ensure_ascii=False)}\n\n"
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yield _sse_event("process_step", call_step)
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step_index += 1
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# Phase 2: execute tools — parallel when multiple, sequential when single
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if len(tool_calls_list) > 1:
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with app.app_context():
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tool_results = executor.process_tool_calls_parallel(
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tool_calls_list, context, max_workers=4
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)
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else:
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with app.app_context():
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tool_results = executor.process_tool_calls(
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tool_calls_list, context
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)
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# Execute tools with error wrapping
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tool_results = self._execute_tools_safe(
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app, executor, tool_calls_list, context
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)
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# Phase 3: emit all tool_result steps (after execution, same order)
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# Emit tool_result steps
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for tr in tool_results:
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skipped = False
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try:
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result_content = json.loads(tr["content"])
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skipped = result_content.get("skipped", False)
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except Exception:
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skipped = False
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result_step = {
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'id': f'step-{step_index}',
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'index': step_index,
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'type': 'tool_result',
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'id_ref': tr['tool_call_id'],
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'name': tr['name'],
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'content': tr['content'],
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'skipped': skipped,
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"id": f"step-{step_index}",
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"index": step_index,
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"type": "tool_result",
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"id_ref": tr["tool_call_id"],
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"name": tr["name"],
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"content": tr["content"],
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"skipped": skipped,
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}
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all_steps.append(result_step)
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yield f"event: process_step\ndata: {json.dumps(result_step, ensure_ascii=False)}\n\n"
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yield _sse_event("process_step", result_step)
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step_index += 1
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# Append assistant message + tool results for the next iteration
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messages.append({
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"role": "assistant",
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"content": full_content or None,
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"tool_calls": tool_calls_list
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"tool_calls": tool_calls_list,
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})
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messages.extend(tool_results)
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all_tool_results.extend(tool_results)
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total_completion_tokens += token_count
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continue
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# --- No tool calls: final iteration — save message to DB ---
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suggested_title = None
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# prompt_tokens already holds the last iteration's value (set during streaming)
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total_completion_tokens += token_count
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with app.app_context():
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# Build content JSON with ordered steps array for DB storage.
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# 'steps' is the single source of truth for rendering order.
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content_json = {
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"text": full_content,
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}
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if all_tool_calls:
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content_json["tool_calls"] = self._build_tool_calls_json(all_tool_calls, all_tool_results)
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# Store ordered steps — the single source of truth for rendering order
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content_json["steps"] = all_steps
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msg_id = str(uuid.uuid4())
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suggested_title = self._save_message(
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app, conv_id, conv_model, msg_id,
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full_content, all_tool_calls, all_tool_results,
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all_steps, total_prompt_tokens, total_completion_tokens,
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)
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msg = Message(
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id=msg_id,
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conversation_id=conv_id,
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role="assistant",
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content=json.dumps(content_json, ensure_ascii=False),
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token_count=total_completion_tokens,
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)
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db.session.add(msg)
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db.session.commit()
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# Auto-generate title from first user message if needed
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conv = db.session.get(Conversation, conv_id)
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# Record token usage (get user_id from conv, not g —
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# app.app_context() creates a new context where g.current_user is lost)
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if conv:
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record_token_usage(conv.user_id, conv_model, prompt_tokens, total_completion_tokens)
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if conv and (not conv.title or conv.title == "新对话"):
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user_msg = Message.query.filter_by(
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conversation_id=conv_id, role="user"
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).order_by(Message.created_at.asc()).first()
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if user_msg and user_msg.content:
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try:
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content_data = json.loads(user_msg.content)
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title_text = content_data.get("text", "")[:30]
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except (json.JSONDecodeError, TypeError):
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title_text = user_msg.content.strip()[:30]
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if title_text:
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suggested_title = title_text
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else:
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suggested_title = "新对话"
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db.session.refresh(conv)
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conv.title = suggested_title
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db.session.commit()
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else:
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suggested_title = None
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yield f"event: done\ndata: {json.dumps({'message_id': msg_id, 'token_count': total_completion_tokens, 'suggested_title': suggested_title}, ensure_ascii=False)}\n\n"
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yield _sse_event("done", {
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"message_id": msg_id,
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"token_count": total_completion_tokens,
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"suggested_title": suggested_title,
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})
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return
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yield f"event: error\ndata: {json.dumps({'content': 'exceeded maximum tool call iterations'}, ensure_ascii=False)}\n\n"
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yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
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def safe_generate():
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"""Wrapper that catches client disconnection during yield."""
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try:
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yield from generate()
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except (ClientDisconnected, BrokenPipeError, ConnectionResetError):
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pass # Client aborted, silently stop
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pass
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return Response(
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safe_generate(),
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@ -308,16 +224,224 @@ class ChatService:
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"X-Accel-Buffering": "no",
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"Connection": "keep-alive",
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"Transfer-Encoding": "chunked",
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}
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},
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)
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# ------------------------------------------------------------------
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# Private helpers — extracted for testability and readability
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# ------------------------------------------------------------------
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def _stream_llm_response(
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self, app, conv_id, messages, tools, tool_choice, step_index
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):
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"""Call LLM streaming API and parse the response.
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Returns a tuple of parsed results, or None if the client disconnected.
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Raises HTTPError / ConnectionError / Timeout for the caller to handle.
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"""
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full_content = ""
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full_thinking = ""
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token_count = 0
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prompt_tokens = 0
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tool_calls_list = []
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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sse_chunks = [] # Collect SSE events to yield later
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with app.app_context():
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active_conv = db.session.get(Conversation, conv_id)
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resp = self.llm.call(
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model=active_conv.model,
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messages=messages,
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max_tokens=active_conv.max_tokens,
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temperature=active_conv.temperature,
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thinking_enabled=active_conv.thinking_enabled,
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tools=tools,
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tool_choice=tool_choice,
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stream=True,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
for line in resp.iter_lines():
|
||||
if _client_disconnected():
|
||||
resp.close()
|
||||
return None
|
||||
|
||||
if not line:
|
||||
continue
|
||||
line = line.decode("utf-8")
|
||||
if not line.startswith("data: "):
|
||||
continue
|
||||
data_str = line[6:]
|
||||
if data_str == "[DONE]":
|
||||
break
|
||||
try:
|
||||
chunk = json.loads(data_str)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
usage = chunk.get("usage", {})
|
||||
if usage:
|
||||
token_count = usage.get("completion_tokens", 0)
|
||||
prompt_tokens = usage.get("prompt_tokens", 0)
|
||||
|
||||
choices = chunk.get("choices", [])
|
||||
if not choices:
|
||||
continue
|
||||
|
||||
delta = choices[0].get("delta", {})
|
||||
|
||||
reasoning = delta.get("reasoning_content", "")
|
||||
if reasoning:
|
||||
full_thinking += reasoning
|
||||
if thinking_step_id is None:
|
||||
thinking_step_id = f"step-{step_index}"
|
||||
thinking_step_idx = step_index
|
||||
sse_chunks.append(_sse_event("process_step", {
|
||||
"id": thinking_step_id, "index": thinking_step_idx,
|
||||
"type": "thinking", "content": full_thinking,
|
||||
}))
|
||||
|
||||
text = delta.get("content", "")
|
||||
if text:
|
||||
full_content += text
|
||||
if text_step_id is None:
|
||||
text_step_idx = step_index + (1 if thinking_step_id is not None else 0)
|
||||
text_step_id = f"step-{text_step_idx}"
|
||||
sse_chunks.append(_sse_event("process_step", {
|
||||
"id": text_step_id, "index": text_step_idx,
|
||||
"type": "text", "content": full_content,
|
||||
}))
|
||||
|
||||
tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list)
|
||||
|
||||
return (
|
||||
full_content, full_thinking, tool_calls_list,
|
||||
thinking_step_id, thinking_step_idx,
|
||||
text_step_id, text_step_idx,
|
||||
token_count, prompt_tokens,
|
||||
sse_chunks,
|
||||
)
|
||||
|
||||
def _execute_tools_safe(self, app, executor, tool_calls_list, context):
|
||||
"""Execute tool calls with top-level error wrapping.
|
||||
|
||||
If an unexpected exception occurs during tool execution, it is
|
||||
converted into error tool results instead of crashing the stream.
|
||||
"""
|
||||
try:
|
||||
if len(tool_calls_list) > 1:
|
||||
with app.app_context():
|
||||
tool_results = executor.process_tool_calls_parallel(
|
||||
tool_calls_list, context, max_workers=TOOL_MAX_WORKERS
|
||||
)
|
||||
else:
|
||||
with app.app_context():
|
||||
tool_results = executor.process_tool_calls(
|
||||
tool_calls_list, context
|
||||
)
|
||||
except Exception as e:
|
||||
logger.exception("Error during tool execution")
|
||||
tool_results = [
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": tc["id"],
|
||||
"name": tc["function"]["name"],
|
||||
"content": json.dumps({
|
||||
"success": False,
|
||||
"error": f"Tool execution failed: {e}",
|
||||
}, ensure_ascii=False),
|
||||
}
|
||||
for tc in tool_calls_list
|
||||
]
|
||||
|
||||
# Truncate oversized tool result content
|
||||
for tr in tool_results:
|
||||
if len(tr["content"]) > TOOL_RESULT_MAX_LENGTH:
|
||||
try:
|
||||
result_data = json.loads(tr["content"])
|
||||
original = result_data
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
original = None
|
||||
|
||||
tr["content"] = json.dumps(
|
||||
{"success": False, "error": "Tool result too large, truncated"},
|
||||
ensure_ascii=False,
|
||||
) if not original else json.dumps(
|
||||
{
|
||||
**original,
|
||||
"truncated": True,
|
||||
"_note": f"Content truncated, original length {len(tr['content'])} chars",
|
||||
},
|
||||
ensure_ascii=False,
|
||||
default=str,
|
||||
)[:TOOL_RESULT_MAX_LENGTH]
|
||||
|
||||
return tool_results
|
||||
|
||||
def _save_message(
|
||||
self, app, conv_id, conv_model, msg_id,
|
||||
full_content, all_tool_calls, all_tool_results,
|
||||
all_steps, total_prompt_tokens, total_completion_tokens,
|
||||
):
|
||||
"""Save the final assistant message and auto-generate title if needed.
|
||||
|
||||
Returns the suggested_title or None.
|
||||
"""
|
||||
suggested_title = None
|
||||
with app.app_context():
|
||||
content_json = {"text": full_content}
|
||||
if all_tool_calls:
|
||||
content_json["tool_calls"] = self._build_tool_calls_json(
|
||||
all_tool_calls, all_tool_results
|
||||
)
|
||||
content_json["steps"] = all_steps
|
||||
|
||||
msg = Message(
|
||||
id=msg_id,
|
||||
conversation_id=conv_id,
|
||||
role="assistant",
|
||||
content=json.dumps(content_json, ensure_ascii=False),
|
||||
token_count=total_completion_tokens,
|
||||
)
|
||||
db.session.add(msg)
|
||||
db.session.commit()
|
||||
|
||||
conv = db.session.get(Conversation, conv_id)
|
||||
|
||||
if conv:
|
||||
record_token_usage(
|
||||
conv.user_id, conv_model,
|
||||
total_prompt_tokens, total_completion_tokens,
|
||||
)
|
||||
|
||||
if conv and (not conv.title or conv.title == "新对话"):
|
||||
user_msg = Message.query.filter_by(
|
||||
conversation_id=conv_id, role="user"
|
||||
).order_by(Message.created_at.asc()).first()
|
||||
if user_msg and user_msg.content:
|
||||
try:
|
||||
content_data = json.loads(user_msg.content)
|
||||
title_text = content_data.get("text", "")[:30]
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
title_text = user_msg.content.strip()[:30]
|
||||
suggested_title = title_text or "新对话"
|
||||
db.session.refresh(conv)
|
||||
conv.title = suggested_title
|
||||
db.session.commit()
|
||||
|
||||
return suggested_title
|
||||
|
||||
def _build_tool_calls_json(self, tool_calls: list, tool_results: list) -> list:
|
||||
"""Build tool calls JSON structure - matches streaming format"""
|
||||
"""Build tool calls JSON structure - matches streaming format."""
|
||||
result = []
|
||||
for i, tc in enumerate(tool_calls):
|
||||
result_content = tool_results[i]["content"] if i < len(tool_results) else None
|
||||
|
||||
# Parse result to extract success/skipped status
|
||||
success = True
|
||||
skipped = False
|
||||
execution_time = 0
|
||||
|
|
@ -327,10 +451,9 @@ class ChatService:
|
|||
success = result_data.get("success", True)
|
||||
skipped = result_data.get("skipped", False)
|
||||
execution_time = result_data.get("execution_time", 0)
|
||||
except:
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Keep same structure as streaming format
|
||||
result.append({
|
||||
"id": tc.get("id", ""),
|
||||
"type": tc.get("type", "function"),
|
||||
|
|
@ -345,9 +468,8 @@ class ChatService:
|
|||
})
|
||||
return result
|
||||
|
||||
|
||||
def _process_tool_calls_delta(self, delta: dict, tool_calls_list: list) -> list:
|
||||
"""Process tool calls from streaming delta"""
|
||||
"""Process tool calls from streaming delta."""
|
||||
tool_calls_delta = delta.get("tool_calls", [])
|
||||
for tc in tool_calls_delta:
|
||||
idx = tc.get("index", 0)
|
||||
|
|
@ -355,7 +477,7 @@ class ChatService:
|
|||
tool_calls_list.append({
|
||||
"id": tc.get("id", ""),
|
||||
"type": tc.get("type", "function"),
|
||||
"function": {"name": "", "arguments": ""}
|
||||
"function": {"name": "", "arguments": ""},
|
||||
})
|
||||
if tc.get("id"):
|
||||
tool_calls_list[idx]["id"] = tc["id"]
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import os
|
|||
import re
|
||||
import time
|
||||
import requests
|
||||
from typing import Optional, List
|
||||
from typing import Optional, List, Union
|
||||
|
||||
|
||||
def _resolve_env_vars(value: str) -> str:
|
||||
|
|
@ -59,7 +59,8 @@ class LLMClient:
|
|||
raise ValueError(f"Model '{model}' has no api_key configured")
|
||||
return api_url, api_key
|
||||
|
||||
def _build_body(self, model, messages, max_tokens, temperature, thinking_enabled, tools, stream, api_url):
|
||||
def _build_body(self, model, messages, max_tokens, temperature, thinking_enabled,
|
||||
tools, tool_choice, stream, api_url):
|
||||
"""Build request body with provider-specific parameter adaptation."""
|
||||
provider = _detect_provider(api_url)
|
||||
|
||||
|
|
@ -79,23 +80,17 @@ class LLMClient:
|
|||
|
||||
# --- Provider-specific: thinking ---
|
||||
if thinking_enabled:
|
||||
if provider == "glm":
|
||||
if provider == "glm" or provider == "deepseek":
|
||||
body["thinking"] = {"type": "enabled"}
|
||||
elif provider == "deepseek":
|
||||
pass # deepseek-reasoner has built-in reasoning, no extra param
|
||||
else:
|
||||
raise NotImplementedError(f"Thinking not supported for provider '{provider}'")
|
||||
|
||||
# --- Provider-specific: tools ---
|
||||
if tools:
|
||||
body["tools"] = tools
|
||||
body["tool_choice"] = "auto"
|
||||
body["tool_choice"] = tool_choice if tool_choice is not None else "auto"
|
||||
|
||||
# --- Provider-specific: stream ---
|
||||
if stream:
|
||||
body["stream"] = True
|
||||
if provider == "glm":
|
||||
body["stream_options"] = {"include_usage": True}
|
||||
elif provider == "deepseek":
|
||||
pass # DeepSeek does not support stream_options
|
||||
|
||||
return body
|
||||
|
||||
|
|
@ -107,15 +102,16 @@ class LLMClient:
|
|||
temperature: float = 1.0,
|
||||
thinking_enabled: bool = False,
|
||||
tools: Optional[List[dict]] = None,
|
||||
tool_choice: Optional[Union[str, dict]] = None,
|
||||
stream: bool = False,
|
||||
timeout: int = 120,
|
||||
timeout: int = 200,
|
||||
max_retries: int = 3,
|
||||
):
|
||||
"""Call LLM API with retry on rate limit (429)"""
|
||||
api_url, api_key = self._get_credentials(model)
|
||||
body = self._build_body(
|
||||
model, messages, max_tokens, temperature,
|
||||
thinking_enabled, tools, stream, api_url,
|
||||
thinking_enabled, tools, tool_choice, stream, api_url,
|
||||
)
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
|
|
|
|||
|
|
@ -638,6 +638,99 @@ buffer 拼接: "event: process_step\ndata: {\"id\":\"step-0\",...}\n\n"
|
|||
|
||||
---
|
||||
|
||||
## Token 用量计算
|
||||
|
||||
### 术语定义
|
||||
|
||||
| 术语 | 说明 |
|
||||
| --- | --- |
|
||||
| `prompt_tokens` | 发给模型的输入 token 数量（包括 system prompt、历史消息、工具定义、工具结果等全部上下文） |
|
||||
| `completion_tokens` | 模型生成的输出 token 数量（包括 thinking 内容、正文回复、工具调用 JSON） |
|
||||
| `total_tokens` | `prompt_tokens + completion_tokens` |
|
||||
|
||||
### 计算流程
|
||||
|
||||
一次完整的对话可能经历多轮工具调用迭代，每轮都会向 LLM 发送请求并收到响应。Token 用量计算分为三个阶段：
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[LLM SSE Stream] -->|usage 字段| B["_stream_llm_response()"]
|
||||
B -->|每轮累加| C["generate() 循环"]
|
||||
C -->|最终值| D["_save_message()"]
|
||||
D --> E["record_token_usage()"]
|
||||
E --> F["TokenUsage 表"]
|
||||
```
|
||||
|
||||
#### 1. 流式解析 — 从 SSE chunks 中提取
|
||||
|
||||
LLM API 在流的最后一个 chunk 中返回 `usage` 字段（需要在请求中设置 `stream_options` 才有，否则为空）：
|
||||
|
||||
```python
|
||||
# chat.py: _stream_llm_response()
|
||||
usage = chunk.get("usage", {})
|
||||
if usage:
|
||||
token_count = usage.get("completion_tokens", 0) # 本轮输出 token
|
||||
prompt_tokens = usage.get("prompt_tokens", 0) # 本轮输入 token
|
||||
```
|
||||
|
||||
#### 2. 迭代累加 — generate() 循环
|
||||
|
||||
每轮迭代结束后，将本轮的 prompt 和 completion token 累加到总计：
|
||||
|
||||
```python
|
||||
# chat.py: generate()
|
||||
total_prompt_tokens += prompt_tokens # 累加每轮 prompt
|
||||
total_completion_tokens += completion_tokens # 累加每轮 completion
|
||||
```
|
||||
|
||||
#### 3. 记录到数据库
|
||||
|
||||
最终调用 `record_token_usage()` 写入 TokenUsage 表，同时 Message 表也记录 completion token：
|
||||
|
||||
```python
|
||||
# chat.py: _save_message()
|
||||
msg = Message(token_count=total_completion_tokens) # Message 表仅记录 completion
|
||||
record_token_usage(user_id, model, total_prompt_tokens, total_completion_tokens)
|
||||
```
|
||||
|
||||
### 多轮迭代示例
|
||||
|
||||
一次涉及工具调用的对话（如：用户提问 → LLM 调用搜索 → LLM 生成回复）：
|
||||
|
||||
```
|
||||
迭代 1: prompt=800, completion=150 (LLM 决定调用 web_search)
|
||||
迭代 2: prompt=1500, completion=300 (LLM 根据搜索结果生成最终回复)
|
||||
|
||||
─────────────────────────────────────────
|
||||
累加结果:
|
||||
total_prompt_tokens = 800 + 1500 = 2300
|
||||
total_completion_tokens = 150 + 300 = 450
|
||||
─────────────────────────────────────────
|
||||
```
|
||||
|
||||
> **注意**：`prompt_tokens` 的累加意味着存在重复计算 — 第 2 轮的 prompt 包含了第 1 轮的上下文，累加后 `total_prompt_tokens` 大于本次对话的真实输入 token 总量（历史部分被多次计算）。这是因为每轮请求是独立的 API 调用，各自计费。如果需要精确的单次对话输入 token，可以只取最后一轮的 `prompt_tokens`。
|
||||
|
||||
### 存储位置
|
||||
|
||||
| 位置 | 存什么 | 粒度 |
|
||||
| --- | --- | --- |
|
||||
| `Message.token_count` | `total_completion_tokens`（仅输出） | 单条消息 |
|
||||
| `TokenUsage` 表 | `prompt_tokens` + `completion_tokens` + `total_tokens` | 按 user + 日期 + model 聚合 |
|
||||
|
||||
`TokenUsage` 按 **user_id + 日期 + model** 维度聚合，同一天同一模型的多次对话会累加到同一条记录：
|
||||
|
||||
```python
|
||||
# helpers.py: record_token_usage()
|
||||
if existing:
|
||||
existing.prompt_tokens += prompt_tokens
|
||||
existing.completion_tokens += completion_tokens
|
||||
existing.total_tokens += prompt_tokens + completion_tokens
|
||||
else:
|
||||
create new TokenUsage record
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 分页机制
|
||||
|
||||
所有列表接口使用**游标分页**：
|
||||
|
|
|
|||
Loading…
Reference in New Issue